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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly
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About the Role This is an opportunity to work as part of the team and project “Development of Multi-Modal Foundational Models and AI Accelerators for Zero-shot Intelligent Surveillance System
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to self-organize into complex structures. Our approach is to develop sophisticated mathematical models – informed by state-of-the-art biological knowledge and experimental data – to understand
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models
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candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy systems. Responsibilities
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pathogens as a model system. There are two post-doctoral research positions and one PhD studentship associated with Dr. McDonald’s UKRI Future Leader Fellowship, which will explore the cell biology
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, to contribute to cutting-edge research on the early detection and prevention of primary intestinal tumours using animal models of ageing. The project involves a range of advanced techniques, such as complex mouse
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for carrying out research to develop iPSC-derived lung cell models. Working within a team of biochemists, cell and structural biologists, you will perform experimental work to apply omics technologies, advanced
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navigation algorithms and machine learning models on physical robot platforms. We are particularly interested in candidates with expertise in generative AI and curriculum learning applied to robotics, as
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megakaryocyte cell communication and coordination. We will employ a range of approaches including advanced microscopy (confocal and intravital), models of thrombus formation (ex vivo and in vivo ), and flow